J. Biomedical Science and Engineering, 2008, 1, 170-172
Published Online November 2008 in SciRes. http://www.srpublishing.org/journal/jbise JBiSE
Brain initiated interaction
Rajesh Singla1& Dr Balraj Gupta
1Department of Instrumentation and Control Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar-144011, India. Correspondence should be
addressed to Rajesh Singla ([e-mail]).
Received March 4, 2008; revised September 5, 2008; accepted September 5, 2008
ABSTRACT
Brain-Computer Interfaces (BCI) are developed
to help locked-in patients, who lose control of
their bodies and are unable to perform simple
tasks such as speech, locomotion, and can’t
even effectively interact, with their environment.
BCI shows promise in allowing these individuals
to interact with a computer using EEG. A Brain
Computer Interface is a communication system
in which messages or commands that an indi-
vidual sends to the external world do not pass
through the brain’s normal output pathways of
peripheral nerves and muscles. A system is
created to allow individuals with motor disabili-
ties to control the motion of the bed on which
they are bedridden via BCI for drug delivery and
other activities, with the help of eye motion and
changes in the absolute power in alpha rhythms
of an EEG signal of the patient.
Keywords: BCI, eye events, EEG, Lab VIEW
1. INTRODUCTION
BCI (Brain Computer Interface) research is a multidisci-
plinary field requiring the knowledge of neuroscience,
physiology, psychology and engineering. For the devel-
opment of BCI, we generally use the Electroencephalo-
gram (EEG). EEG signal is composed of electrical
rhythms and transient discharges. Features like wave
shape, amplitude, frequency and power are detected
which are typical for a particular act and it can vary from
person to person. Once these features are detected, they
can be used to generate a control signal by using Trans-
lation algorithm and can be used to operate some devices.
Brain-Computer Interface (BCI) shows a great potential
to provide new channels for physically disabled people,
especially locked in patients, to communicate and inter-
act with the outside environment. EEG-based BCI is
non-invasive, so it is more readily accepted. In this paper,
we introduce the design of a BCI based on changes in
EEG amplitude due to eye activity and in the absolute
power of alpha rhythms after eye activity, followed by
applications based on this core technology i.e. control-
ling the motion of the bed of severely paralyzed patients
for drug and food delivery etc with the help of a stepper
motor installed for controlling the motion of the bed in
both directions i.e. up and down.
2. SYSTEM ARCHITECTURE
Figure 1 shows the complete experimental setup of a
BCI system, designed to control environment (in this
case motion of the bed).
The system is developed using virtual instrumentation
technology and consists of basic two modules: hardware
and software. The hardware set up of the system consists
of
• EEG equipment (Head box and adaptor box)
• Desktop PC interfaced to EEG Hardware via USB
port.
• USB based digital output signal interfacing board
(National Instruments 6015) for motion control.
Figure 1. Complete Experimental setup of a BCI system.
Figure 2. International system of electrode placement.
SciRes Copyright © 2008

R. Singla et al/ J. Biomedical Science and Engineering 1 (2008) 170-172 171
SciRes Copyright © 2008 JBiSE
The software module was developed program for BCI
using LABVIEW to perform the function of EEG acqui-
sition analysis and display.
2.1. EEG Equipment
The device used for study is RMS 32 Brain View Plus. It
can record 32 channels of EEG data from electrodes
placed according to the international 10-20 system. The
voltage generated by the brain cells and picked up by
EEG is extremely small (between 10-20 microvolt) and
amplification is needed of the order of ten thousand
times for successful recording of the EEG signal. The
odd numbered electrodes are placed on the left side of
the head while even numbered electrodes are placed on
the right side of the scalp. The view of the electrode po-
sitions as seen from the side and top is as shown in the
following figure.
The standard parts of the EEG hardware include adap-
tor box, head box, connecting cable and PC. The Head
Box is used for connecting electrodes from the scalp to
the hardware unit. The signal generated is amplified and
then sent to adaptor Box for signal conditioning. The
digital signal generated then, passes to the PC where it is
displayed on the screen on Super Spec software designed
for display of EEG Signals.
2.2. Software Design of BCI
Software for the Brain computer interface is designed on
the Lab VIEW platform which consists of software front
panel for user interaction and block diagram program-
ming code to control the overall functionality of the sys-
tem. Figure 3 shows the functional elements of BCI sys-
tem.
Figure 3. Functional elements of a BCI system EEG hardware
interfaced to PC
For easy understanding and debugging, the software
code is divided into three sub modules namely:
• EEG signal acquisition and processing module
• Feature extraction module.
• Device control module
EEG signal acquisition and processing module ac-
quires online EEG signal from EEG machine channel
(FP2-F4), (FP1-F3) the channel being more sensitive to
eye events. Also the signal is acquired from (O2-CAR),
CAR being the Common Averaged Reference, the chan-
nel most sensitive for variations in power of alpha
rhythms. The raw sampled EEG data file created by EEG
machine Super Spec software at the sampling rate of 256
Hz is then read continuously at the start of acquisition.
The raw data is then processed using as series of filters.
The signal is fed to a band pass filter implemented using
low pass filter (4th order FIR filter with cut off 99Hz) and
high pass filter (4th order FIR filter with cut off 0.1Hz) to
limit the EEG signal bandwidth (0.1 to 99 Hz). A 50 Hz
notch filter is used to remove power line interference.
The processed EEG data is fed to feature extraction
module which executes an amplitude and
time-duration-based algorithm to detect the changes in
the EEG signal due to eye events such as eye open and
eye close. Once the event of eye open and eye close is
detected the systems then checks the absolute power in
the signal of (O2-CAR) channel in the frequency range
of 8 to 12 Hz of 512 samples with the help of FFT. If the
event eye open is detected and then the power in the fre-
quency range of 8 to 12 Hz is less than 0.5 V2 the device
control module is executed to send a high Boolean data
type signal to a switch connected to digital output line
P03 of USB based interfacing board through DAQ assis-
tant that moves the stepper motor in anti clockwise di-
rection for 33 steps. Similarly If the event eye close is
detected and the power in the frequency range of 8 to 12
Hz is more than 2.5 V2 of 512 samples the device control
module is executed to send a low Boolean data type sig-
nal to a switch connected to digital output line P03 of
USB based interfacing board through DAQ assistant that
moves the stepper motor in clockwise direction for 33
steps. If the condition for particular event is not met the
DAQ assistant is configured to send simultaneously the
low Boolean data type signal at the particular output
digital lines to make the bed remain in rest position.
Once the stepper motor has taken 33 steps for the bed
motion no further action is taken for a time period of 5
minutes what so ever may be the signal changes i.e. ACQ
switch is made OFF. After 5 minutes Acquisition again
starts and control action is taken accordingly.
The overall software program functionality is con-
trolled by the customized design of soft front panel using
controls and indicators, on PC screen, through which the
user interacts with the BCI system. It consists of ‘ACQ
switch’ to start and stop the acquisition of EEG signal
data file, ‘EEG recorder’ a calibrated waveform chart to
show graphical record of EEG signal at the time of ac-
quisition of (FP2-F4 and O2-CAR) and one virtual bed
EEG
Head
Box
EEG
Adaptor
Box
Feature Extraction sub
module
Data Acquisition &
p
rocessin
g
sub module
Device control sub
module
USB based Digital
output interfacing board
connected to PC
Front panel
User Interface
for BCI
User com-
mands
Di
g
ital si
g
nals
Software module
Signal to stepper motorvia
switch for motion control
PC
Screen

172 R. Singla et al/ J. Biomedical Science and Engineering 1 (2008) 170-172
SciRes Copyright © 2008 JBiSE
that can be moved up and down for 33 steps to depict the
status of the particular eye event and power in the alpha
rhythms.
3. RESULTS
Many factors determine the performance of a BCI sys-
tem. These factors include the brain signals measured,
the signal processing methods that extract signal features,
the algorithms that translate these features into device
commands, the output devices that execute these com-
mands, the feedback provided to the user and the charac-
teristics of the user. The parameters of the features ex-
tracted vary from individual to individual so it is impor-
tant to develop the generalized BCI. The changes in the
EEG due to eye motion are detected from the waveforms
originating at FP2-F4, FP1-F3. The amount of change in
the amplitude during eye open and eye close vary from
subject to subject, location of electrodes on the forehead,
physiological state of the patient and contact impedance
of the electrodes on the scalp. The wave can be recon-
structed and hence can be used for further control action
in the development of BCI. The results are taken online.
As soon as the BCI is switched on, the EEG pattern from
the machine is recorded on the front panel and changes
due to eye events are detected and displayed on the front
panel. The changes in the EEG patterns are detected and
intelligent control action is taken we found that the pro-
duction of changes due to eye events is not the same for
all the cases. For some person the amplitude is different,
for some latency is different. To remove this problem we
used a sensitivity factor. The function of the sensitivity
factor is to vary the threshold values for the eye events.
Normally it is observed that if we keep a factor of 250 on
positive side and –125 on negative sides the detection is
almost clear. In some cases we have to increase sensitiv-
ity factor. A factor of 100 is provided which seems to
work best. If we increase the sensitivity the factor on
positive side now goes to 350 and on negative side it
goes to –225. Similarly we can further increase or de-
crease the sensitivity factor depending on the patient’s
context.
4. CONCLUSION AND FUTURE SCOPE
The key is to take BCI technology beyond the demon-
stration stage to the real world applications, so that the
quality of life for paralyzed patients is improved. We
detected the changes in the EEG patterns due to eye
events. We have used eye events and power in the alpha
rhythms for control of bed motion to facilitate the drug
and food delivery to the patients. The possibility of ex-
panding the BCI into latest technology will enhance the
adoption of this technology and develop into feasible
solutions with further advances. It can be further used to
design a virtual keyboard which can enable the locked in
patients to interact with PC.
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